Severe Weather: Evolution, Prediction, and Risk Reduction

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: closed (24 May 2024) | Viewed by 155

Special Issue Editor

Servei Meteorologic de Catalunya, 08029 Barcelona, Spain
Interests: severe weather; remote sensing; nowcasting; hail; heavy rain; supercells; squall lines
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Severe weather is usually defined as the occurrence of large hail, tornadoes, strong convective winds, or heavy rains in thunderstorms. The increase in social networks and the improvement of mobile cameras have allowed the identification of a larger number of cases in recent years. However, the increment in remote sensing systems (weather radar, lightning location systems, or satellite) has also helped us to intervene in an increasing number of cases, not only associated with the human capabilities of observing them. In this way, the new systems have allowed us to have a better understanding of the processes associated with severe weather conditions. However, there are many issues that should be minimized to reduce the impact of severe thunderstorms in urban areas, especially if the current high risk increases in the short term because of atmospheric conditions and human activity. This Special Issue welcomes all manuscripts involving the analysis of severe weather cases, climatology of severe phenomena in a region, or new advances in techniques for modelling, identifying, or nowcasting severe weather.

Dr. Tomeu Rigo
Guest Editor

Manuscript Submission Information

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Keywords

  • hail
  • tornadoes
  • straight convective winds
  • heavy rain
  • remote sensing
  • numerical models
  • supercells
  • squall lines
  • risk

Published Papers (1 paper)

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Research

18 pages, 4992 KiB  
Article
Assessment of Satellite Products in Estimating Tropical Cyclone Remote Precipitation over the Yangtze River Delta Region
by Xinyue Wu, Yebing Liu, Shulan Liu, Yubing Jin and Huiyan Xu
Atmosphere 2024, 15(6), 667; https://doi.org/10.3390/atmos15060667 - 31 May 2024
Abstract
Satellite products have shown great potential in estimating torrential rainfall due to their wide and consistent global coverage. This study assessed the monitoring capabilities of satellite products for the tropical cyclone remote precipitation (TRP) over the Yangtze River Delta region (YRDR) associated with [...] Read more.
Satellite products have shown great potential in estimating torrential rainfall due to their wide and consistent global coverage. This study assessed the monitoring capabilities of satellite products for the tropical cyclone remote precipitation (TRP) over the Yangtze River Delta region (YRDR) associated with severe typhoon Khanun (2017) and super-typhoon Mangkhut (2018). The satellite products include the CPC MORPHing technique (CMORPH) data, Tropical Rainfall Measuring Mission 3B42 Version 7 (TRMM 3B42), and Integrated Multi-satellite Retrievals for the Global Precipitation Measurement Mission (GPM IMERG). Eight precision evaluation indexes and statistical methods were used to analyze and evaluate the monitoring capabilities of CMORPH, TRMM 3B42, and GPM IMERG satellite precipitation products. The results indicated that the monitoring capability of TRMM satellite precipitation products was superior in capturing the spatial distribution, and GPM products captured the temporal distributions and different category precipitation observed from gauge stations. In contrast, the CMORPH products performed moderately during two heavy rainfall events, often underestimating or overestimating precipitation amounts and inaccurately detecting precipitation peaks. Overall, the three satellite precipitation products showed low POD, high FAR, low TS, and high FBIAS for heavy rainfall events, and the differences in monitoring torrential TRP may be related to satellite retrieval algorithms. Full article
(This article belongs to the Special Issue Severe Weather: Evolution, Prediction, and Risk Reduction)
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